something here
The p-value of the test is less than the significance level alpha = 0.05. We can conclude that hr sleep and stress sleep are significantly correlated with a correlation coefficient beetween 0.64–0.88.
The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
---
title: "Sleep Dashboard "
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
theme:
bg: "#101010"
fg: "#FDF7F7"
primary: "#ED79F9"
logo: logo.png
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(readr)
library(tidyverse)
library(hrbrthemes)
library(viridis)
library(lubridate)
library(reshape2)
library(ggplot2)
library(ggpubr)
library(plotly)
library(GGally)
setwd("~/Desktop/module_3/all_data")
Lorena <- read_csv("Lorena_converted (3).csv")
Lorena$ID<-"W1"
Martin <- read_csv2("Martin.csv")
Martin$ID<-"M1"
Nely <- read_csv2("Nely.csv")
Nely$ID<-"W2"
dd<-merge(Nely, Martin, all = TRUE)
dd<-merge(dd, Lorena, all = TRUE)
```
Overview
=======================================================================
Row
-----------------------------------------------------------------------
### Global sleep overview
```{r}
dd1<-dd[,c(1,11,12,13,14,98)]
dd1<-as.data.frame(dd1)
dd1<-melt(dd1, id=c("calendar_date","ID"))
p<-ggplot(dd1, aes(x=calendar_date,y=round(value*100),group=variable, fill=variable)) +
geom_density(stat = "identity",alpha=.6)+theme_ipsum() +scale_fill_ipsum()+
labs(x = "Date",
y = "Sleep in %",
color = "Sleep variable")+facet_grid(~ID)
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```
> something here
Row
-----------------------------------------------------------------------
### total sleep overview
```{r }
p<-ggplot(dd, aes(x = calendar_date, y = total_sleep_hours)) +
geom_line()+geom_point()+geom_smooth()+theme_ipsum()+facet_grid(~ID)
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```
Corr_Night analysis
=======================================================================
Row
-----------------------------------------------------------------------
### **Influence from Median_hr_sleep on sleep quality**
```{r }
p<- ggplot(dd,aes(x=perceived_sleep_quality, y=median_hr_sleep, fill=factor(perceived_sleep_quality))) +
geom_boxplot() +geom_point()+
scale_fill_ipsum()+
labs(x = "perceived_sleep_quality",
y = "median_hr_sleep",
color = "Sleep variable")+ theme_ipsum() +scale_fill_ipsum()
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```
### **Influence from median_stress_sleep on sleep quality**
```{r}
p<- ggplot(dd,aes(x=perceived_sleep_quality, y=median_stress_sleep, fill=factor(perceived_sleep_quality))) +
geom_boxplot() +geom_point()+
scale_fill_ipsum()+
labs(x = "perceived_sleep_quality",
y = "median_stress_sleep",
color = "Sleep variable")+ theme_ipsum() +scale_fill_ipsum()
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```
Row
-----------------------------------------------------------------------
### **Correlation stress & HR**
```{r}
p<-ggscatter(dd, x = "median_stress_sleep", y = "median_hr_sleep", color="ID",
add = "reg.line", conf.int = FALSE,
cor.coef = FALSE, cor.method = "pearson")+
stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```
> The p-value of the test is less than the significance level alpha = 0.05. We can conclude that hr sleep and stress sleep are significantly correlated with a correlation coefficient beetween 0.64--0.88.
### **Correlation vigorous_activity_hours & total_sleep_hours**
```{r}
p<-ggscatter(dd, x = "vigorous_activity_hours", y = "total_sleep_hours", color="ID",
add = "reg.line", conf.int = FALSE,
cor.coef = FALSE, cor.method = "pearson")+
stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```
>The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
### **Correlation steps & total_sleep_hours**
```{r}
p<-ggscatter(dd, x = "steps", y = "total_sleep_hours", color="ID",
add = "reg.line", conf.int = FALSE,
cor.coef = FALSE, cor.method = "pearson")+
stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```
>The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
Global corr analysis
=======================================================================
Row {}
-----------------------------------------------------------------------
```{r}
dd2<-dd[,c(6,42,59,69,69,94:98)]
ggpairs(dd2)
```
Row {}
-----------------------------------------------------------------------
```{r}
dd2<-dd[,c(6,12,27,94:96)]
ggpairs(dd2)
```